Building Change Detection with Deep Learning by Fusing Spectral and Texture Features of Multisource Remote Sensing Images: A GF-1 and Sentinel 2B Data Case
文献类型：期刊论文
| 作者 | Fan, Junfu; Zhang, Mengzhen; Chen, Jiahao; Zuo, Jiwei; Shi, Zongwen; Ji, Min |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-04-29 |
| 卷号 | 15期号:9页码:2351 |
| 关键词 | building change detection deep learning high-resolution multispectral multisource spectral data |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs15092351 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Building change detection is an important task in the remote sensing field, and the powerful feature extraction ability of the deep neural network model shows strong advantages in this task. However, the datasets used for this study are mostly three-band high-resolution remote sensing images from a single data source, and few spectral features limit the development of building change detection from multisource remote sensing images. To investigate the influence of spectral and texture features on the effect of building change detection based on deep learning, a multisource building change detection dataset (MS-HS BCD dataset) is produced in this paper using GF-1 high-resolution remote sensing images and Sentinel-2B multispectral remote sensing images. According to the different resolutions of each Sentinel-2B band, eight different multisource spectral data combinations are designed, and six advanced network models are selected for the experiments. After adding multisource spectral and texture feature data, the results show that the detection effects of the six networks improve to different degrees. Taking the MSF-Net network as an example, the F1-score and IOU improved by 0.67% and 1.09%, respectively, compared with high-resolution images, and by 7.57% and 6.21% compared with multispectral images. |
| 学科主题 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS关键词 | NETWORK ; CNN |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/193828]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Shandong University of Technology 2.Chinese Academy of Sciences 3.Institute of Geographic Sciences & Natural Resources Research, CAS 4.Central China Normal University 5.Shandong University of Science & Technology |
| 推荐引用方式 GB/T 7714 | Fan, Junfu,Zhang, Mengzhen,Chen, Jiahao,et al. Building Change Detection with Deep Learning by Fusing Spectral and Texture Features of Multisource Remote Sensing Images: A GF-1 and Sentinel 2B Data Case[J]. REMOTE SENSING,2023,15(9):2351. |
| APA | Fan, Junfu,Zhang, Mengzhen,Chen, Jiahao,Zuo, Jiwei,Shi, Zongwen,&Ji, Min.(2023).Building Change Detection with Deep Learning by Fusing Spectral and Texture Features of Multisource Remote Sensing Images: A GF-1 and Sentinel 2B Data Case.REMOTE SENSING,15(9),2351. |
| MLA | Fan, Junfu,et al."Building Change Detection with Deep Learning by Fusing Spectral and Texture Features of Multisource Remote Sensing Images: A GF-1 and Sentinel 2B Data Case".REMOTE SENSING 15.9(2023):2351. |
入库方式： OAI收割
来源：地理科学与资源研究所
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